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The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
In this paper, we propose 3 different machine learning techniques such as Random Forest, Bagging and Support Vector Machine along with time domain feature for classifying sleep stages based on single-channel EEG. Whole-night polysomnograms from 25 subjects were recorded employing R&K standard. The evolved process investigated the EEG signals of (C4-A1) for sleep staging. Automatic and manual scoring...
The P300 Speller is a Brain Computer Interface that enables communication using the EEG signal. The P300 wave is an Event Related Potential that occurs as a response to a familiar stimulus. This system can be used to aid persons who are unable to communicate via conventional methods. In this paper, the P300 Speller has been modified to allow communication in three languages: English, Sinhala and Tamil...
Epilepsy is a chronic neurological disorder which occurs due to the recurring evoking of seizure which results due to the abnormal rhythmic discharge of electrical activities of the brain. This fluctuation in the electrical activities of the brain can be analyzed using EEG signal which provides valuable information about the physiological states of the brain. In this paper we propose an efficient...
In Brain Computer Interface (BCI), the thoughts of a subject is read to provide an appropriate way of communication using only brain signals. The Information of electroencephalogram (EEG) signals defer between subjects depending on their thoughts according to research. In this paper, a comparison between different types of features tested by several classifiers is done to propose a model for classifying...
In recent years, there has been a growing increase in the use of electroencephalographic (EEG) signals for biometric systems. In investigating the use of EEG-based biometrics in a smart-device environment, this study focused on the development of a specific feature selection method, and on the feasibility of nonlinear dynamic characteristics of EEG signals for identifying individuals. We recorded...
The primary prerequisite for development of foot prosthetics driven by brain computer interface (BCI) is classification of left and right lower limb movement from brain signals. Moreover, it is essential to detect best possible combination of feature extraction and classification technique which will efficiently recognize left and right lower limb movement intentions from brain signals in as minimum...
Epilepsy is a neurological disorder disease that affects more than 55 million people in the world. In this paper, we have proposed an efficient intelligent pattern recognition system for the classification of epileptic and non-epileptic electroencephalogram (EEG) signals. For this purpose, we used state-of-the-art machine learning technique, i.e., SVM (support vector machines) to classify epileptic...
We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already...
The use of large number of channels in EEG based Motor-imagery Brain Computer Interfaces (BCI) may cause long preparation time and redundancy of data. In this paper, we propose a Cohen's d effect-size based channel selection algorithm which eliminates the redundant channels while improving the classification performance. This method (referred to as Effect-size based CSP (E-CSP)) eliminates the channels...
This paper presents an approach for automated staging of the human sleep. It is based on analysis of two channel electroencephalogram and an electrooculogram. The classifier is trained with two different groups of features separately and in combination as well. Statistic measures of first and higher order serve as features from the first set. The rules of Rechtschaffen and Kales are exploited for...
In this paper, a statistical method has been proposed to identify motor imagery left and right hand movements from electroencephalogram (EEG) signals in the Dual Tree Complex Wavelet Transform (DTCWT) domain. The total experiment is carried out with the publicly available benchmark BCI-competition 2003 Graz motor imagery dataset. First, the EEG signals are decomposed into several bands of real and...
Brain activities are often investigated through Electroencephalographic (EEG) data analysis using timedomain Independent Component Analysis (ICA). Nevertheless, the instantaneous mixing model of ICA cannot properly describe spatio-temporal dynamics, such as those related to traveling waves of neural activity. In this work, we exploit the application of the Complex ICA (cICA) to describe the underlying...
Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented...
Epilepsy is a crucial neurological disorder in which patients experience epileptic seizure caused by abnormal electrical discharges from the brain. It is highly common in children and adults at the age of 65–70. Around 1 % of the world's population is affected by this disease. The mechanism of epilepsy is still incomprehensible to researchers; however, 80% of the seizure activity can be treated effectively...
In this paper the problematic of epileptic detection is treated. An algorithm of EEG signal classification into two classes: Healthy and Epileptics is developed. The difference with conventional methods is the use of free seizure epileptic records. A good classification accuracy means that it is possible to detect an epileptic in normal state or at an early stage of epilepsy. The raw EEG signal is...
Brain-Machine Interface is a direct communication pathway between brain and an external electronic device. BMIs aim to translate brain activities into control commands. To design a system that translates brain waves and its activities to desired commands, motor imagery tasks classification is the core part. Classification accuracy not only depends on how capable the classifier is but also it is about...
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